NVIDIA is quietly completing a strategic identity shift, evolving from a supporter of the open-source AI ecosystem into a direct participant and builder.

According to reports from August 11, NVIDIA aims for its next-generation Nemotron 4 to be among the world's most powerful open models. Employees on the project anticipate the largest version will have at least one trillion total parameters, roughly double the size of the Nemotron 3 Ultra released in June.

Preliminary decisions have been made regarding the model's pre-training data and architecture, but the final specifications and release date remain undetermined. The most critical training round has not yet started. The so-called "trillion-parameter Nemotron 4" is currently a research and development plan, not a released product.

This significant investment could easily be misinterpreted as NVIDIA trying to become the next OpenAI or Anthropic. However, NVIDIA does not need to recoup its costs through API fees from Nemotron. By offering a free model that enables more enterprises, governments, and startups to train, fine-tune, and deploy AI, NVIDIA can generate revenue across multiple layers: GPUs, NVLink networks, entire systems, CUDA, and enterprise software.

Nemotron 4 functions more like a demand generator for compute power: the model is free, but the computation required to use it is not. NVIDIA needs to expand its customer base from a few dominant players to the entire market.

The current GPU boom is exceptionally concentrated. NVIDIA's 10-Q filing for the period ending April 26 shows that three direct customers contributed 21%, 17%, and 16% of quarterly revenue, totaling 54%. The company also disclosed that one AI research and deployment company indirectly contributed "meaningful revenue" through cloud services. While these direct customers might be system integrators, distributors, or cloud providers, the concentration risk is clearly documented.

A more significant challenge is that major customers are simultaneously developing NVIDIA alternatives. Amazon AWS uses its Trainium chips to handle Anthropic's training and inference, officially stating that nearly one million Trainium 2 chips are already serving Claude. Google's Ironwood TPU is designed for large-scale training and inference. AMD is also competing for open model workloads with its MI350 series and ROCm software.

Closed-source models concentrate demand within a few labs and cloud platforms, which possess immense scale, strong bargaining power, and the incentive to move stable, mature workloads to their own custom chips. For NVIDIA, the safest market scenario is not one single model company dominating AI, but thousands of diverse models and applications growing simultaneously. The more fragmented the demand, the harder it is for customers to use a single custom ASIC to cover all architectures. The faster workloads change, the more advantageous NVIDIA's general-purpose GPUs and mature CUDA software ecosystem become.

This explains why NVIDIA cannot merely pay lip service to the open ecosystem. An open model that lags far behind closed-source models will only attract research experiments, making it difficult for banks, manufacturers, and government agencies to justify budgets for production clusters. A model must be sufficiently powerful to encourage enterprises to continue training it with their own data and to trust it with high-frequency workflows. Only then can an open model transition from developer downloads into actual GPU utilization in data centers.

The trillion-parameter scale serves this purpose. It is not a medal certifying intelligence level, but a tool for NVIDIA to cross the threshold of enterprise usability. The existing Nemotron 3 Ultra has 550 billion total parameters, activates 55 billion parameters per token, and supports a context window of up to one million tokens. NVIDIA's own tests show it approaches other leading open models on some tasks, with significantly higher inference throughput. However, third-party rankings cited by The Information indicate it still lags behind the strongest open models from China and is far from the top tier globally. Therefore, parameter count only indicates ambition, not results.

In a sparse MoE architecture, the total parameters and the actual parameters called per inference differ significantly. Data quality, training tokens, post-training, tool-calling capabilities, and inference systems collectively determine the final outcome. If Nemotron 4 only offers the impressive number of "1 trillion" without being a top-tier performer on real-world tasks, it is unlikely to change any procurement decisions.

A free model can sell an entire AI factory. NVIDIA has already paved the path for conversion. The open Nemotron provides model weights, data, and training recipes. Enterprises use NeMo for continued pre-training, fine-tuning, reinforcement learning, and evaluation. For deployment, they connect to NIM inference microservices, TensorRT-LLM, and CUDA. As scale increases, compute power falls onto DGX Cloud, public cloud GPUs, or enterprise-built HGX/DGX clusters. Beyond GPUs, NVLink, InfiniBand or Spectrum-X networking, CPUs, DPUs, and the NVIDIA AI Enterprise subscription also appear on the bill.

Early examples of this path already exist. Palantir has brought Nemotron into the U.S. government's isolated networks. Customers can train models on local infrastructure, retain model weights, and iterate continuously, with production deployment supported by NVIDIA AI Enterprise. The Japanese market provides a template for "sovereign AI": research institutions and enterprises use Nemotron data and NeMo to train Japanese language models, with HGX B300 handling private infrastructure and Jetson managing edge deployment. Closed-source APIs cannot enter isolated networks and struggle to meet data residency, model ownership, and local audit requirements. Open models bring these workloads, which would not typically occur on public cloud APIs, into government data centers, enterprise data centers, regional AI clouds, and edge devices.

For model companies, this is a more fragmented and harder-to-monetize market. For NVIDIA, which sells infrastructure, every additional deployment location creates a new revenue stream for hardware and software. Model routing expands this business further. The Information reports that alongside the Nemotron 3.5 Lightning, NVIDIA also released free model routing software. This router directs simple tasks to cheaper, smaller models and only routes the few difficult tasks to the large model. On the surface, it helps customers use less expensive compute power. The actual bet is that as the cost per task decreases, enterprises will launch more persistent agents, transforming a single Q&A session into a continuous process involving planning, searching, tool calling, verification, and rework.

Gartner predicts that by 2030, the inference cost of a trillion-parameter model will decrease by over 90% from 2025 levels. However, each task for an agent could consume 5 to 30 times more tokens than a standard chatbot. If token usage growth outpaces the unit price decline, total inference spending could still rise. NVIDIA's bet is not that "the more expensive the large model, the more GPUs are sold," but rather "the cheaper the intelligence, the faster its usage expands." By covering lightweight models, flagship models, and routing tools with Nemotron, NVIDIA is proactively creating the demand elasticity that follows a price drop.

Building models also serves to get the next-generation chips right, ahead of time. There is another layer of value in Nemotron that is not directly reflected in model revenue: it serves as NVIDIA's own full-stack stress test. If a chip company waits for customer models to be finalized before adapting to new attention mechanisms, MoE routing, long contexts, and low-precision formats, hardware iteration will always be a step behind. By training cutting-edge models themselves, engineering teams can identify bottlenecks in compute, memory, interconnects, data throughput, and inference scheduling much earlier. These findings are then fed back into the next generation of GPUs, NVLink, and software libraries.

The Nemotron 3 Ultra already shows strong signs of "hardware co-design". The model uses a hybrid Mamba-Attention and MoE architecture, pre-training utilizes the NVFP4 format designed for Blackwell, and multi-token prediction boosts generation speed. NVIDIA states that the quantized version can be deployed on four B200 GPUs. Such a model serves as both a product and a demonstration load for Blackwell's low-precision capabilities, memory architecture, and inference software. After being open-sourced, external developers will continue to optimize this load for NVIDIA.

New model architectures are first run on CUDA, PyTorch, vLLM, SGLang, and TensorRT-LLM. Optimizations are then fed back into the NVIDIA platform. Subsequent developers are not faced with a simple GPU performance gap, but a production environment that has been refined by thousands of projects. In a case study NVIDIA showed this year, the per-token cost of running DeepSeek V4 on Blackwell dropped by as much as one-fifth within a month. Combined with multiple software optimizations, throughput was increased by up to 20 times.